Part of #134054.
This corresponds to the pytorch mypy changes from D61493706. Updating takes so
long and touches so many files that it's impossible to land as a whole without conflicting with some other intermediate change.
So landing these 'type: ignore' for pytorch in advance of them actually being needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134202
Approved by: https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
Summary:
This diff is reverting D44897935
D44897935: [FSDP] Include duplicate parameters and modules when calling named_parameters and named_modules (#98912) by fegin has been identified to be causing the following test or build failures:
Tests affected:
- [caffe2/torch/fb/module_factory/sync_sgd/tests:test_pyper_data_parallel_wrapper - caffe2.torch.fb.module_factory.sync_sgd.tests.test_pyper_data_parallel_wrapper.PyPerDataParallelWrapperTest: test_fsdp_submodules_pyper](https://www.internalfb.com/intern/test/562950025957458/)
Here's the Multisect link:
https://www.internalfb.com/multisect/1893714
Here are the tasks that are relevant to this breakage:
We're generating a revert to back out the changes in this diff, please note the backout may land if someone accepts it.
If you believe this diff has been generated in error you may Commandeer and Abandon it.
Test Plan: NA
Reviewed By: fegin
Differential Revision: D45027286
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99353
Approved by: https://github.com/izaitsevfb, https://github.com/fegin
The default option of `named_parameters` and `named_modules` is to remove the duplicated parameters and modules. However, in FSDP, we need to know what parameters are shared. As a result, setting `remove_duplicate` to False is required in FSDP. Without setting `remove_duplicate` to False, FSDP won't be able to discover shared weights in some cases (e.g., the shared weights are in the same module or there are shared modules).
Differential Revision: [D44897935](https://our.internmc.facebook.com/intern/diff/D44897935/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98912
Approved by: https://github.com/awgu
I made a pass over Linjian's `_symbolic_trace.py` and tidied it up a bit. Aside from simple stylistic changes, this PR makes the following changes:
- Save `visited_params: Set[nn.Parameter]` to avoid linear overhead to check a parameter already being visited when appending to the parameter execution order list (`param_forward_order`)
- Move the tracer patching logic to a class `_ExecOrderTracer` to have a reference to `self.exec_info` without having a fragmented 2-step initialization (like the old `_init_execution_info(root_module)` plus `_patch_tracer(tracer, root_module, execution_info)`)
- Define `_ParamUsageInfo` to formalize the `Tuple[nn.Module, List[str, nn.Parameter]]` elements being mapped to in the execution info `dict`, and clarify the documentation regarding what this represents
- Change the unit test to use `TestCase`, not `FSDPTest`, to avoid initializing a process group
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89917
Approved by: https://github.com/zhaojuanmao, https://github.com/fegin
